Strengthening Immunization Data: Protocol for the Evaluation of an Electronic Immunization Register
Bibliographic record
Abstract
BACKGROUND: Electronic immunization registers (EIRs) can strengthen immunization systems and, if used effectively, can lead to greater efficiency and improvements in vaccination coverage. Several low- and middle-income countries introduced EIRs for COVID-19 vaccination and are now integrating them for routine immunization. OBJECTIVE: This study aims to describe the protocol used to evaluate the implementation of an EIR in the Lao People's Democratic Republic in 2022. In addition, it seeks to identify opportunities to improve implementation, scale-up, and sustainability in the country. METHODS: To evaluate the implementation of the EIR in the Lao People's Democratic Republic, we will (1) map the EIR workflow process, (2) examine EIR user and stakeholder perspectives, and (3) assess the EIR data quality. Data will be collected and analyzed through a mixed methods approach. This evaluation will involve a document review, observation of workflows in health facilities, health facility user surveys, key informant interviews with decision makers, and an assessment of immunization data quality. This protocol details the methods for each of these components. The evidence generated will be triangulated to identify the strengths and weaknesses of the early implementation phase of the EIR, facilitators of and barriers to the implementation, and whether the introduction of the EIR has improved immunization data processes and quality compared with paper-based processes. RESULTS: Data collection took place between April 2024 and August 2024. Data analysis is currently ongoing, with results expected to be shared with study stakeholders in October 2024. CONCLUSIONS: This early evaluation will contribute to developing a road map for the nationwide strengthening and sustainability of the EIR based on challenges and lessons learned, potentially streamlining further implementation efforts and enabling more effective use of the EIR. This paper presents a methodology for evaluating EIR implementation that can be replicated in other low- and middle-income countries implementing EIRs. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/65663.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.272 | 0.315 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.012 | 0.017 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.011 | 0.013 |
| Insufficient payload (model declined to judge) | 0.103 | 0.037 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".